Lactase - A Conversion Story

Walaa Abdallah, Manhattan College, Chemical Engineering

Author Profile
Initial Publication Date: October 12, 2023

Summary

Students will run an experiment to determine the kinetics of the enzyme lactase known for its conversion of lactose to glucose. The experiment will consist of three parts:

  1. Hands-on lab experiment
  2. Data analysis using linearization methods
  3. Data analysis using MATLAB curve-fitting app

PART I: The first part of this module involves a hands-on portion where students will collect data monitoring the conversion of lactose to glucose as a function of time. This will be completed by adding a lactase tablet to a lactose source and recording the glucose concentration (using a glucose meter) for a fixed period of time. The source of the lactose can include:

  • Skim milk
  • Lactose powder

PART II: The second part of this module will require students to analyze their data (from part I) using Michaelis-Menten linearization methods, in particular, Hanes-woolf. This will allow for the determination of the kinetic parameters, KM and Vmax. Data analysis will be completed using Excel.

 

PART III: The third part of this module will allow students to fit their data (from part I) on MATLAB and determine the kinetic parameters, KM and Vmax, using the pre-made template (the foundation of this is based on the curve-fitting / Michaelis-Menten equation). Students will then compare their results to those from part II.

 

Key words: enzymes, lactase, Michaelis-Menten, Hanes-woolf

Share your modifications and improvements to this activity through the Community Contribution Tool »

Learning Goals

By the end of this course, students will be able to:

  1. Understand how enzymes work, the Michaelis-Menten equation, its limitations, and what the kinetic parameters signify.
  2. Analyze experimental data using linearization methods in Excel.
  3. Determine kinetic parameters using experimental data as inputs for MATLAB.

The experimental data collected in the lab is used as input in both Excel and MATLAB. The Excel component focuses on the steps to determine kinetics parameters, the errors associated with Michaelis-Menten / linearization methods, and standard deviation. The MATLAB component focuses on curve fitting and computation.

MATLAB is utilized as a tool to aid with data analysis. Its curve-fitting app should be used to understand the kinetics of lactase, how the parameters are determined and what they signify. It is also used as a reference for data analyzed using Excel. This module improves student learning as it has both a lab component and a data analysis component which allows students to run the full experiment from start to finish. The students are responsible for setting up the experiment, collecting data, analyzing data using two methods, and comparing the results.

The higher-order thinking skills include critical thinking, computation, data analysis, and model development.


Instructors can add to this module by requiring students to complete an oral or written report, but as it stands, the skills focus on engineering and problem-solving.

Context for Use

Module targeted towards high school students; no limit on class sizes as long as sufficient instructors are available

Module is a lab activity followed by data analysis on both Excel and MATLAB; lab experiment takes approximately 60 minutes with data analysis ranging depending on student expertise. However, it is estimated that a minimum of 60 minutes will be required for the Excel data analysis and an additional 60 minutes for MATLAB.

Students do not need previous MATLAB experience - a handout will be provided outlining the steps necessary to utilize the MATLAB application for curve-fitting.

Instructors should briefly describe how enzymes work and the reaction being studied before students complete the module. This will allow students to take the concepts / theory learned and apply it to the hands-on experiment and data analysis.

This module is targeted toward science teachers and can be presented in either chemistry or biology classes. Theoretically, it can also be adapted into a computational course and can be modified if needed for other courses in the STEM fields.

Students do not need any MATLAB skills before starting this module.

Description and Teaching Materials

All in handouts.

Handout #1 - Enzymes.docx (Microsoft Word 2007 (.docx) 1.2MB Nov8 23)

Handout #2 - Lab Procedure.docx (Microsoft Word 2007 (.docx) 10kB Nov8 23)

Handout #3 - Michaelis-Menten Kinetics.docx (Microsoft Word 2007 (.docx) 554kB Nov9 23)

Handout #4 - Excel Data-Fitting.xlsx (Excel 2007 (.xlsx) 109kB Nov8 23)

MATLAB File data.csv (Comma Separated Values 482bytes Nov8 23)

MATLAB App - curve_fittinged.mlapp ( 54kB Nov8 23)

Teaching Notes and Tips

1. Module can be broken up into multiple sections:

  1. Running the experiment (60 minutes)
  2. Data fitting on Excel (60 minutes)
  3. Data fitting on MATLAB (60 minutes)

2. Excel is used as a tool to ensure students understand how MATLAB performs curve-fitting - the MATLAB portion does not require the students to have previous MATLAB knowledge.

 


Assessment

  1. Analyze student data from lab (the trend should be clear - glucose concentration increases with time). Students should be able to explain how the substrate was converted into a product and the trend.
  2. Assess student linearization figure on Excel - should be reproducible and resemble Hanes-woolf plot.
  3. Compare student parameters with those determined using MATLAB - should be similar if student completed calculations correctly.

References and Resources